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Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, or time-series distribution and stationarity checks.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | analyze |
| description | Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, or time-series distribution and stationarity checks. |
Analyze is the research diagnostics boundary. Use it to understand factors,
returns, attribution, robustness, and time-series behavior after data and
signals have been produced. Strategy execution and portfolio construction live
in skills.backtest.
from skills.analyze.factor_analysis import IC_stat, group_stat, full_stat
from skills.analyze.ts_analysis import TimeSeriesAnalyzer, analyze_time_series
from skills.analyze.attribution_counterfactual import performance_metrics
| Module family | Purpose |
|---|---|
factor_analysis | IC statistics, grouped returns, winsorization, drawdown helpers |
ts_analysis | KDE/QQ plots, Hurst, ADF, KPSS, trend scoring |
attribution_* | Symbol/category PnL, Brinson, decision edges, ranking buckets, Shapley, robustness, stat tests |
tearsheet | Pool summary report helpers |
Factor evaluation
from skills.analyze.factor_analysis import IC_stat, group_stat
ic_stat_dict, ic_series = IC_stat(df, rank_IC=True, n=5)
group_return, turnover = group_stat(df, n=5, g=5, verbose=True)
Time-series stationarity check
from skills.analyze.ts_analysis import TimeSeriesAnalyzer
analyzer = TimeSeriesAnalyzer(price_series)
analyzer.analyze_windows([60, 120, 240])
results_df = analyzer.get_results_dataframe()
Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending.
Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.
Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.
Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory.
Use when tasks need reusable research pipeline templates, factor screening, parameter sensitivity sweeps, or strategy comparison.